# Processing Tool Parameters

The Processing parameters control the way that images are processed by the tool. Changing these parameters does not require the tool to be retrained; the effect can be seen right away by reprocessing the database.

Parameter | Description
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Sampling Density | Specifies the density of sampling points with respect to the tool's Feature Size (FS) setting. The sampling density determines the degree of overlap between adjacent samples. A sampling rate of 1 means that the sampling location is incremented by the feature size between samples. The default sampling rate for most tools is 3, which means that the sampling location is incremented by one third of the feature size. Tip: One key aspect of the Sampling Density parameter is the relationship between the tool's processing time and accuracy. A tool's processing time depends approximately quadratically to the Sampling Density (Sd) value. For example, a Sampling Density setting of 1 is approximately nine times faster than a setting of 3 (n2). While higher Sampling Density settings are more accurate, it significantly impacts the processing time of the tool.
Note: For the Green Classify tool, there is a fixed limit to the number of samples that can be processed for a single runtime image, which is 50,000. The number of samples is determined by the Feature Size, the image size, and the Sampling Density, as shown in the following equation: If you have the Masking Mode parameter enabled, the sample count is reduced by the proportion of the pixels that are masked. When the combination of image size, Feature Size and Sampling Density settings exceeds the limit, the following error message may be displayed: Failed to process database sample 'image_file.name' (maximal number of samples exceeded, please reduce the sampling density, view size and/or feature size). In this event, consider options to reduce the number of pixels being processed, either through the use of a mask, focusing the ROI to specific areas or using a larger feature size setting.
Threshold | This parameter is used by all of the tools. For the Blue Locate Tool, the Threshold determines how closely the found feature must be to the desired feature in order for it to be considered detected. The found feature must exceed the setting. For the Blue Read Tool, the Threshold determines how closely the found character must be to the desired character for it to be considered. The found character must exceed the setting. For the Red Analyze Tool, there are two settings, T1 and T2 (expressed as [T1,T2]. They determine the threshold which determines whether or not regions are detected and marked as good or bad. Values below T1 will be classified as good, and values above T2 will be classified as bad. The T1 and T2 values can also be set interactively using the Scores graphic in the Database Overview. For the Green Classify Tool, the Threshold parameter determines the minimum score of a view to be classified.
Precision | The Blue Locate tool includes a unique parameter, Precision, which refines the position of a feature iteratively. With each iteration, a new feature position is computed. The Precision parameter controls when this refinement is terminated. Feature position refinement is repeated until the difference between the current position and the new, refined position is less than the specified precision, multiplied by the Feature Size. For example, if you have a Feature Size of 40, and a Precision setting of 5%, the tool will stop searching for the position of the feature when the next jump would be less than 2 pixels (0.05 * 40 = 2 pixels).
| If Precision is equal to 400%, the Blue Locate tool will stop at D0. 200% = D1 100% = Matches with precision
Stop criteria: |
Feature Filter | Specifies a filter for the Blue Locate and Blue Read tools, based on feature id, x, y, size and orientation, which is used as criteria for found features. By specifying a filter for features, features that do not match the filter will be removed from the results. If the parameter is left blank, all features will be returned. Since this filtering is done after the tool has processed the images, the features are not removed, simply hidden from the results. Features that are not present due to the filter will reappear, without reprocessing the image, by removing the filter criteria. Note: The syntax for filters is the same as that used for Display Filters. For more information about constructing the syntax for a filter, see the Custom Display Filters. For example, if the Feature Filter parameter was set to the following:
Feature Filter | Description
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x in [0,view.width] and y in [0,view.height] | With this filter, only features whose center is within the ROI will be returned.
(id = '0' and x < 200) or (x='1' and x > 200) | With this filter, assuming a View that has a width of 400, features that have an ID of 0 and are on the left, and features that have an ID of 1 and are on the right, will be returned.
Iterations | Specifies the number of times the Red Analyze tool will search locally to find stronger anomalies. In contrast to the Sampling Density parameter, processing time only depends linearly on the number of iterations.
Simple Regions | Specifies that the Red Analyze tool should only extract only "simple regions", i.e. polygons without holes.
Region Filter | Specifies a filter for the Red Analyze tool to be used as criteria for found regions. By specifying a filter, regions that do not match the filter will be removed from the results. If the parameter is left blank, all regions will be returned. Note: The syntax for filters is the same as that used for Display Filters. For more information about constructing the syntax for a filter, see the Custom Display Filters. The available region properties are: score area perimeter compactness x y